Overview

Brought to you by YData

Dataset statistics

Number of variables29
Number of observations249
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory185.0 KiB
Average record size in memory761.0 B

Variable types

Numeric17
Categorical11
DateTime1

Alerts

State Code has constant value "4" Constant
County Code has constant value "13" Constant
Site Num has constant value "3002" Constant
Address has constant value "1645 E ROOSEVELT ST-CENTRAL PHOENIX STN" Constant
State has constant value "Arizona" Constant
County has constant value "Maricopa" Constant
City has constant value "Phoenix" Constant
NO2 Units has constant value "Parts per billion" Constant
O3 Units has constant value "Parts per million" Constant
SO2 Units has constant value "Parts per billion" Constant
CO Units has constant value "Parts per million" Constant
CO 1st Max Value is highly overall correlated with CO AQI and 9 other fieldsHigh correlation
CO AQI is highly overall correlated with CO 1st Max Value and 9 other fieldsHigh correlation
CO Mean is highly overall correlated with CO 1st Max Value and 9 other fieldsHigh correlation
NO2 1st Max Value is highly overall correlated with CO 1st Max Value and 4 other fieldsHigh correlation
NO2 AQI is highly overall correlated with CO 1st Max Value and 4 other fieldsHigh correlation
NO2 Mean is highly overall correlated with CO 1st Max Value and 6 other fieldsHigh correlation
O3 1st Max Value is highly overall correlated with O3 AQI and 2 other fieldsHigh correlation
O3 AQI is highly overall correlated with O3 1st Max Value and 2 other fieldsHigh correlation
O3 Mean is highly overall correlated with CO 1st Max Value and 9 other fieldsHigh correlation
SO2 1st Max Value is highly overall correlated with CO 1st Max Value and 5 other fieldsHigh correlation
SO2 AQI is highly overall correlated with CO 1st Max Value and 5 other fieldsHigh correlation
SO2 Mean is highly overall correlated with CO 1st Max Value and 6 other fieldsHigh correlation
Unnamed: 0 is highly overall correlated with CO 1st Max Value and 5 other fieldsHigh correlation
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
Date Local has unique values Unique
NO2 1st Max Hour has 34 (13.7%) zeros Zeros
SO2 Mean has 12 (4.8%) zeros Zeros
SO2 1st Max Value has 12 (4.8%) zeros Zeros
SO2 1st Max Hour has 41 (16.5%) zeros Zeros
SO2 AQI has 29 (11.6%) zeros Zeros
CO 1st Max Hour has 28 (11.2%) zeros Zeros

Reproduction

Analysis started2025-07-09 23:32:22.363613
Analysis finished2025-07-09 23:32:56.788995
Duration34.43 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct249
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499.45783
Minimum1
Maximum997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-07-09T23:32:56.900758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50.6
Q1249
median501
Q3749
95-th percentile947.4
Maximum997
Range996
Interquartile range (IQR)500

Descriptive statistics

Standard deviation289.74371
Coefficient of variation (CV)0.58011647
Kurtosis-1.2051379
Mean499.45783
Median Absolute Deviation (MAD)252
Skewness-0.0045311787
Sum124365
Variance83951.419
MonotonicityStrictly increasing
2025-07-09T23:32:57.044287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
5 1
 
0.4%
9 1
 
0.4%
13 1
 
0.4%
17 1
 
0.4%
21 1
 
0.4%
25 1
 
0.4%
29 1
 
0.4%
33 1
 
0.4%
37 1
 
0.4%
Other values (239) 239
96.0%
ValueCountFrequency (%)
1 1
0.4%
5 1
0.4%
9 1
0.4%
13 1
0.4%
17 1
0.4%
21 1
0.4%
25 1
0.4%
29 1
0.4%
33 1
0.4%
37 1
0.4%
ValueCountFrequency (%)
997 1
0.4%
993 1
0.4%
989 1
0.4%
985 1
0.4%
981 1
0.4%
977 1
0.4%
973 1
0.4%
969 1
0.4%
965 1
0.4%
961 1
0.4%

State Code
Categorical

Constant 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size16.0 KiB
4
249 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters249
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 249
100.0%

Length

2025-07-09T23:32:57.181015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T23:32:57.261154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 249
100.0%

Most occurring characters

ValueCountFrequency (%)
4 249
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 249
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 249
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 249
100.0%

County Code
Categorical

Constant 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size16.3 KiB
13
249 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters498
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13
2nd row13
3rd row13
4th row13
5th row13

Common Values

ValueCountFrequency (%)
13 249
100.0%

Length

2025-07-09T23:32:57.342843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T23:32:57.407440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
13 249
100.0%

Most occurring characters

ValueCountFrequency (%)
1 249
50.0%
3 249
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 498
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 249
50.0%
3 249
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 498
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 249
50.0%
3 249
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 498
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 249
50.0%
3 249
50.0%

Site Num
Categorical

Constant 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size16.8 KiB
3002
249 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters996
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3002
2nd row3002
3rd row3002
4th row3002
5th row3002

Common Values

ValueCountFrequency (%)
3002 249
100.0%

Length

2025-07-09T23:32:57.475288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T23:32:57.533678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3002 249
100.0%

Most occurring characters

ValueCountFrequency (%)
0 498
50.0%
3 249
25.0%
2 249
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 996
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 498
50.0%
3 249
25.0%
2 249
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 996
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 498
50.0%
3 249
25.0%
2 249
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 996
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 498
50.0%
3 249
25.0%
2 249
25.0%

Address
Categorical

Constant 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size25.3 KiB
1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
249 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters9711
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
2nd row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
3rd row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
4th row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
5th row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN

Common Values

ValueCountFrequency (%)
1645 E ROOSEVELT ST-CENTRAL PHOENIX STN 249
100.0%

Length

2025-07-09T23:32:57.604313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T23:32:57.664849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1645 249
16.7%
e 249
16.7%
roosevelt 249
16.7%
st-central 249
16.7%
phoenix 249
16.7%
stn 249
16.7%

Most occurring characters

ValueCountFrequency (%)
E 1245
12.8%
1245
12.8%
T 996
 
10.3%
O 747
 
7.7%
S 747
 
7.7%
N 747
 
7.7%
L 498
 
5.1%
R 498
 
5.1%
5 249
 
2.6%
4 249
 
2.6%
Other values (10) 2490
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9711
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1245
12.8%
1245
12.8%
T 996
 
10.3%
O 747
 
7.7%
S 747
 
7.7%
N 747
 
7.7%
L 498
 
5.1%
R 498
 
5.1%
5 249
 
2.6%
4 249
 
2.6%
Other values (10) 2490
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9711
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1245
12.8%
1245
12.8%
T 996
 
10.3%
O 747
 
7.7%
S 747
 
7.7%
N 747
 
7.7%
L 498
 
5.1%
R 498
 
5.1%
5 249
 
2.6%
4 249
 
2.6%
Other values (10) 2490
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9711
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1245
12.8%
1245
12.8%
T 996
 
10.3%
O 747
 
7.7%
S 747
 
7.7%
N 747
 
7.7%
L 498
 
5.1%
R 498
 
5.1%
5 249
 
2.6%
4 249
 
2.6%
Other values (10) 2490
25.6%

State
Categorical

Constant 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size17.5 KiB
Arizona
249 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1743
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArizona
2nd rowArizona
3rd rowArizona
4th rowArizona
5th rowArizona

Common Values

ValueCountFrequency (%)
Arizona 249
100.0%

Length

2025-07-09T23:32:57.741461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T23:32:57.798269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
arizona 249
100.0%

Most occurring characters

ValueCountFrequency (%)
A 249
14.3%
r 249
14.3%
i 249
14.3%
z 249
14.3%
o 249
14.3%
n 249
14.3%
a 249
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1743
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 249
14.3%
r 249
14.3%
i 249
14.3%
z 249
14.3%
o 249
14.3%
n 249
14.3%
a 249
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1743
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 249
14.3%
r 249
14.3%
i 249
14.3%
z 249
14.3%
o 249
14.3%
n 249
14.3%
a 249
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1743
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 249
14.3%
r 249
14.3%
i 249
14.3%
z 249
14.3%
o 249
14.3%
n 249
14.3%
a 249
14.3%

County
Categorical

Constant 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size17.8 KiB
Maricopa
249 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1992
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaricopa
2nd rowMaricopa
3rd rowMaricopa
4th rowMaricopa
5th rowMaricopa

Common Values

ValueCountFrequency (%)
Maricopa 249
100.0%

Length

2025-07-09T23:32:57.868078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T23:32:57.928401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
maricopa 249
100.0%

Most occurring characters

ValueCountFrequency (%)
a 498
25.0%
M 249
12.5%
r 249
12.5%
i 249
12.5%
c 249
12.5%
o 249
12.5%
p 249
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1992
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 498
25.0%
M 249
12.5%
r 249
12.5%
i 249
12.5%
c 249
12.5%
o 249
12.5%
p 249
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1992
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 498
25.0%
M 249
12.5%
r 249
12.5%
i 249
12.5%
c 249
12.5%
o 249
12.5%
p 249
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1992
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 498
25.0%
M 249
12.5%
r 249
12.5%
i 249
12.5%
c 249
12.5%
o 249
12.5%
p 249
12.5%

City
Categorical

Constant 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size17.5 KiB
Phoenix
249 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1743
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhoenix
2nd rowPhoenix
3rd rowPhoenix
4th rowPhoenix
5th rowPhoenix

Common Values

ValueCountFrequency (%)
Phoenix 249
100.0%

Length

2025-07-09T23:32:58.005100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T23:32:58.063961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
phoenix 249
100.0%

Most occurring characters

ValueCountFrequency (%)
P 249
14.3%
h 249
14.3%
o 249
14.3%
e 249
14.3%
n 249
14.3%
i 249
14.3%
x 249
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1743
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 249
14.3%
h 249
14.3%
o 249
14.3%
e 249
14.3%
n 249
14.3%
i 249
14.3%
x 249
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1743
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 249
14.3%
h 249
14.3%
o 249
14.3%
e 249
14.3%
n 249
14.3%
i 249
14.3%
x 249
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1743
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 249
14.3%
h 249
14.3%
o 249
14.3%
e 249
14.3%
n 249
14.3%
i 249
14.3%
x 249
14.3%

Date Local
Date

Unique 

Distinct249
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
Minimum2000-01-01 00:00:00
Maximum2000-09-16 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-09T23:32:58.160516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:58.326379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

NO2 Units
Categorical

Constant 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size19.9 KiB
Parts per billion
249 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters4233
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion 249
100.0%

Length

2025-07-09T23:32:58.451925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T23:32:58.513428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
parts 249
33.3%
per 249
33.3%
billion 249
33.3%

Most occurring characters

ValueCountFrequency (%)
r 498
11.8%
i 498
11.8%
l 498
11.8%
498
11.8%
P 249
 
5.9%
s 249
 
5.9%
t 249
 
5.9%
a 249
 
5.9%
p 249
 
5.9%
b 249
 
5.9%
Other values (3) 747
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4233
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 498
11.8%
i 498
11.8%
l 498
11.8%
498
11.8%
P 249
 
5.9%
s 249
 
5.9%
t 249
 
5.9%
a 249
 
5.9%
p 249
 
5.9%
b 249
 
5.9%
Other values (3) 747
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4233
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 498
11.8%
i 498
11.8%
l 498
11.8%
498
11.8%
P 249
 
5.9%
s 249
 
5.9%
t 249
 
5.9%
a 249
 
5.9%
p 249
 
5.9%
b 249
 
5.9%
Other values (3) 747
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4233
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 498
11.8%
i 498
11.8%
l 498
11.8%
498
11.8%
P 249
 
5.9%
s 249
 
5.9%
t 249
 
5.9%
a 249
 
5.9%
p 249
 
5.9%
b 249
 
5.9%
Other values (3) 747
17.6%

NO2 Mean
Real number (ℝ)

High correlation 

Distinct233
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.443969
Minimum0.8
Maximum73.285714
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-07-09T23:32:58.606273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile2.794167
Q119.375
median27.041667
Q336.583333
95-th percentile49.625
Maximum73.285714
Range72.485714
Interquartile range (IQR)17.208333

Descriptive statistics

Standard deviation13.655841
Coefficient of variation (CV)0.49758988
Kurtosis0.3043754
Mean27.443969
Median Absolute Deviation (MAD)8.875
Skewness0.12881167
Sum6833.5483
Variance186.482
MonotonicityNot monotonic
2025-07-09T23:32:59.352194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.958333 2
 
0.8%
25.083333 2
 
0.8%
36.875 2
 
0.8%
53.333333 2
 
0.8%
37.666667 2
 
0.8%
26.875 2
 
0.8%
22.416667 2
 
0.8%
25.428571 2
 
0.8%
27.416667 2
 
0.8%
12.666667 2
 
0.8%
Other values (223) 229
92.0%
ValueCountFrequency (%)
0.8 1
0.4%
1.181818 1
0.4%
1.270833 1
0.4%
1.383333 1
0.4%
1.554167 1
0.4%
1.704167 1
0.4%
1.854167 1
0.4%
2.072727 1
0.4%
2.341667 1
0.4%
2.45 1
0.4%
ValueCountFrequency (%)
73.285714 1
0.4%
66.791667 1
0.4%
66.541667 1
0.4%
59.25 1
0.4%
59.041667 1
0.4%
57.833333 1
0.4%
54.5 1
0.4%
53.333333 2
0.8%
53.166667 1
0.4%
50.5 1
0.4%

NO2 1st Max Value
Real number (ℝ)

High correlation 

Distinct91
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.102008
Minimum1.9
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-07-09T23:32:59.507186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile5.64
Q141
median51
Q364
95-th percentile80.6
Maximum124
Range122.1
Interquartile range (IQR)23

Descriptive statistics

Standard deviation22.155902
Coefficient of variation (CV)0.44221585
Kurtosis0.77202823
Mean50.102008
Median Absolute Deviation (MAD)11
Skewness-0.11267654
Sum12475.4
Variance490.88399
MonotonicityNot monotonic
2025-07-09T23:32:59.652072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51 13
 
5.2%
43 13
 
5.2%
58 10
 
4.0%
54 8
 
3.2%
66 8
 
3.2%
48 8
 
3.2%
41 7
 
2.8%
33 7
 
2.8%
64 6
 
2.4%
73 6
 
2.4%
Other values (81) 163
65.5%
ValueCountFrequency (%)
1.9 1
0.4%
3.2 1
0.4%
3.4 1
0.4%
3.7 1
0.4%
3.8 1
0.4%
4.3 1
0.4%
4.6 1
0.4%
4.7 2
0.8%
5.2 1
0.4%
5.4 2
0.8%
ValueCountFrequency (%)
124 1
0.4%
117 1
0.4%
116 1
0.4%
106 1
0.4%
101 2
0.8%
92 1
0.4%
89 1
0.4%
87 2
0.8%
84 1
0.4%
81 2
0.8%

NO2 1st Max Hour
Real number (ℝ)

Zeros 

Distinct22
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.903614
Minimum0
Maximum23
Zeros34
Zeros (%)13.7%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-07-09T23:32:59.771435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median20
Q321
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.9707605
Coefficient of variation (CV)0.64521067
Kurtosis-1.5304351
Mean13.903614
Median Absolute Deviation (MAD)3
Skewness-0.49139676
Sum3462
Variance80.474543
MonotonicityNot monotonic
2025-07-09T23:32:59.871698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
21 43
17.3%
22 35
14.1%
0 34
13.7%
20 29
11.6%
23 25
10.0%
1 11
 
4.4%
6 10
 
4.0%
7 9
 
3.6%
19 9
 
3.6%
5 9
 
3.6%
Other values (12) 35
14.1%
ValueCountFrequency (%)
0 34
13.7%
1 11
 
4.4%
2 4
 
1.6%
3 3
 
1.2%
4 6
 
2.4%
5 9
 
3.6%
6 10
 
4.0%
7 9
 
3.6%
8 8
 
3.2%
9 3
 
1.2%
ValueCountFrequency (%)
23 25
10.0%
22 35
14.1%
21 43
17.3%
20 29
11.6%
19 9
 
3.6%
18 1
 
0.4%
17 1
 
0.4%
16 2
 
0.8%
15 1
 
0.4%
14 1
 
0.4%

NO2 AQI
Real number (ℝ)

High correlation 

Distinct76
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.803213
Minimum1
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-07-09T23:33:00.004121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q139
median48
Q362
95-th percentile79.6
Maximum105
Range104
Interquartile range (IQR)23

Descriptive statistics

Standard deviation21.440029
Coefficient of variation (CV)0.44850602
Kurtosis0.34934847
Mean47.803213
Median Absolute Deviation (MAD)12
Skewness-0.16270051
Sum11903
Variance459.67483
MonotonicityNot monotonic
2025-07-09T23:33:00.145996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 13
 
5.2%
41 13
 
5.2%
55 10
 
4.0%
42 9
 
3.6%
51 8
 
3.2%
64 8
 
3.2%
45 8
 
3.2%
31 7
 
2.8%
39 7
 
2.8%
5 7
 
2.8%
Other values (66) 159
63.9%
ValueCountFrequency (%)
1 1
 
0.4%
3 4
1.6%
4 4
1.6%
5 7
2.8%
6 1
 
0.4%
7 3
1.2%
8 3
1.2%
9 1
 
0.4%
10 1
 
0.4%
11 1
 
0.4%
ValueCountFrequency (%)
105 1
0.4%
104 2
0.8%
102 1
0.4%
101 2
0.8%
91 1
0.4%
88 1
0.4%
86 2
0.8%
83 1
0.4%
80 2
0.8%
79 1
0.4%

O3 Units
Categorical

Constant 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size19.9 KiB
Parts per million
249 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters4233
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million 249
100.0%

Length

2025-07-09T23:33:00.281861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T23:33:00.357642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
parts 249
33.3%
per 249
33.3%
million 249
33.3%

Most occurring characters

ValueCountFrequency (%)
r 498
11.8%
i 498
11.8%
l 498
11.8%
498
11.8%
P 249
 
5.9%
s 249
 
5.9%
t 249
 
5.9%
a 249
 
5.9%
p 249
 
5.9%
m 249
 
5.9%
Other values (3) 747
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4233
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 498
11.8%
i 498
11.8%
l 498
11.8%
498
11.8%
P 249
 
5.9%
s 249
 
5.9%
t 249
 
5.9%
a 249
 
5.9%
p 249
 
5.9%
m 249
 
5.9%
Other values (3) 747
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4233
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 498
11.8%
i 498
11.8%
l 498
11.8%
498
11.8%
P 249
 
5.9%
s 249
 
5.9%
t 249
 
5.9%
a 249
 
5.9%
p 249
 
5.9%
m 249
 
5.9%
Other values (3) 747
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4233
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 498
11.8%
i 498
11.8%
l 498
11.8%
498
11.8%
P 249
 
5.9%
s 249
 
5.9%
t 249
 
5.9%
a 249
 
5.9%
p 249
 
5.9%
m 249
 
5.9%
Other values (3) 747
17.6%

O3 Mean
Real number (ℝ)

High correlation 

Distinct224
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02550143
Minimum0.006167
Maximum0.063167
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-07-09T23:33:00.447299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.006167
5-th percentile0.0086084
Q10.01775
median0.025125
Q30.032292
95-th percentile0.044708
Maximum0.063167
Range0.057
Interquartile range (IQR)0.014542

Descriptive statistics

Standard deviation0.010408272
Coefficient of variation (CV)0.40814464
Kurtosis-0.10922333
Mean0.02550143
Median Absolute Deviation (MAD)0.007333
Skewness0.31460662
Sum6.349856
Variance0.00010833212
MonotonicityNot monotonic
2025-07-09T23:33:00.598150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.026333 3
 
1.2%
0.029375 3
 
1.2%
0.023833 3
 
1.2%
0.006667 2
 
0.8%
0.032167 2
 
0.8%
0.01775 2
 
0.8%
0.023167 2
 
0.8%
0.029875 2
 
0.8%
0.012292 2
 
0.8%
0.01025 2
 
0.8%
Other values (214) 226
90.8%
ValueCountFrequency (%)
0.006167 1
0.4%
0.006333 1
0.4%
0.006667 2
0.8%
0.006958 1
0.4%
0.007 1
0.4%
0.007958 1
0.4%
0.008208 1
0.4%
0.008375 1
0.4%
0.008417 1
0.4%
0.008458 1
0.4%
ValueCountFrequency (%)
0.063167 1
0.4%
0.050042 1
0.4%
0.049292 1
0.4%
0.048167 1
0.4%
0.047667 1
0.4%
0.046708 1
0.4%
0.046375 1
0.4%
0.046333 1
0.4%
0.045708 1
0.4%
0.045625 1
0.4%

O3 1st Max Value
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.047413655
Minimum0.01
Maximum0.088
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-07-09T23:33:00.736801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.0194
Q10.038
median0.049
Q30.059
95-th percentile0.072
Maximum0.088
Range0.078
Interquartile range (IQR)0.021

Descriptive statistics

Standard deviation0.015755234
Coefficient of variation (CV)0.33229318
Kurtosis-0.41216529
Mean0.047413655
Median Absolute Deviation (MAD)0.011
Skewness-0.27175414
Sum11.806
Variance0.00024822739
MonotonicityNot monotonic
2025-07-09T23:33:00.885580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.062 12
 
4.8%
0.054 11
 
4.4%
0.046 9
 
3.6%
0.053 9
 
3.6%
0.043 9
 
3.6%
0.04 7
 
2.8%
0.052 7
 
2.8%
0.051 7
 
2.8%
0.049 6
 
2.4%
0.059 6
 
2.4%
Other values (57) 166
66.7%
ValueCountFrequency (%)
0.01 2
0.8%
0.012 1
 
0.4%
0.013 1
 
0.4%
0.014 2
0.8%
0.015 2
0.8%
0.016 2
0.8%
0.017 1
 
0.4%
0.018 1
 
0.4%
0.019 1
 
0.4%
0.02 3
1.2%
ValueCountFrequency (%)
0.088 1
 
0.4%
0.081 1
 
0.4%
0.076 3
1.2%
0.075 1
 
0.4%
0.074 2
0.8%
0.073 4
1.6%
0.072 2
0.8%
0.071 3
1.2%
0.07 2
0.8%
0.069 2
0.8%

O3 1st Max Hour
Real number (ℝ)

Distinct13
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.46988
Minimum0
Maximum23
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-07-09T23:33:01.016370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q110
median10
Q311
95-th percentile12
Maximum23
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5938694
Coefficient of variation (CV)0.15223379
Kurtosis24.5389
Mean10.46988
Median Absolute Deviation (MAD)1
Skewness0.86843951
Sum2607
Variance2.5404197
MonotonicityNot monotonic
2025-07-09T23:33:01.114183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10 102
41.0%
11 95
38.2%
9 25
 
10.0%
12 11
 
4.4%
14 3
 
1.2%
15 3
 
1.2%
8 2
 
0.8%
13 2
 
0.8%
7 2
 
0.8%
0 1
 
0.4%
Other values (3) 3
 
1.2%
ValueCountFrequency (%)
0 1
 
0.4%
3 1
 
0.4%
7 2
 
0.8%
8 2
 
0.8%
9 25
 
10.0%
10 102
41.0%
11 95
38.2%
12 11
 
4.4%
13 2
 
0.8%
14 3
 
1.2%
ValueCountFrequency (%)
23 1
 
0.4%
16 1
 
0.4%
15 3
 
1.2%
14 3
 
1.2%
13 2
 
0.8%
12 11
 
4.4%
11 95
38.2%
10 102
41.0%
9 25
 
10.0%
8 2
 
0.8%

O3 AQI
Real number (ℝ)

High correlation 

Distinct60
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.11245
Minimum8
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-07-09T23:33:01.245998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile16.4
Q132
median42
Q350
95-th percentile90
Maximum132
Range124
Interquartile range (IQR)18

Descriptive statistics

Standard deviation20.758874
Coefficient of variation (CV)0.47058992
Kurtosis1.9112215
Mean44.11245
Median Absolute Deviation (MAD)9
Skewness1.1827445
Sum10984
Variance430.93085
MonotonicityNot monotonic
2025-07-09T23:33:01.398491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 15
 
6.0%
58 12
 
4.8%
46 11
 
4.4%
42 10
 
4.0%
45 9
 
3.6%
39 9
 
3.6%
47 9
 
3.6%
44 7
 
2.8%
34 7
 
2.8%
43 7
 
2.8%
Other values (50) 153
61.4%
ValueCountFrequency (%)
8 2
 
0.8%
10 1
 
0.4%
11 1
 
0.4%
12 2
 
0.8%
13 2
 
0.8%
14 3
1.2%
15 1
 
0.4%
16 1
 
0.4%
17 3
1.2%
19 6
2.4%
ValueCountFrequency (%)
132 1
 
0.4%
114 1
 
0.4%
101 3
1.2%
100 1
 
0.4%
97 2
0.8%
93 4
1.6%
90 2
0.8%
87 3
1.2%
84 2
0.8%
80 2
0.8%

SO2 Units
Categorical

Constant 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size19.9 KiB
Parts per billion
249 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters4233
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion 249
100.0%

Length

2025-07-09T23:33:01.524820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T23:33:01.587662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
parts 249
33.3%
per 249
33.3%
billion 249
33.3%

Most occurring characters

ValueCountFrequency (%)
r 498
11.8%
i 498
11.8%
l 498
11.8%
498
11.8%
P 249
 
5.9%
s 249
 
5.9%
t 249
 
5.9%
a 249
 
5.9%
p 249
 
5.9%
b 249
 
5.9%
Other values (3) 747
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4233
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 498
11.8%
i 498
11.8%
l 498
11.8%
498
11.8%
P 249
 
5.9%
s 249
 
5.9%
t 249
 
5.9%
a 249
 
5.9%
p 249
 
5.9%
b 249
 
5.9%
Other values (3) 747
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4233
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 498
11.8%
i 498
11.8%
l 498
11.8%
498
11.8%
P 249
 
5.9%
s 249
 
5.9%
t 249
 
5.9%
a 249
 
5.9%
p 249
 
5.9%
b 249
 
5.9%
Other values (3) 747
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4233
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 498
11.8%
i 498
11.8%
l 498
11.8%
498
11.8%
P 249
 
5.9%
s 249
 
5.9%
t 249
 
5.9%
a 249
 
5.9%
p 249
 
5.9%
b 249
 
5.9%
Other values (3) 747
17.6%

SO2 Mean
Real number (ℝ)

High correlation  Zeros 

Distinct193
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2047621
Minimum0
Maximum12.166667
Zeros12
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-07-09T23:33:01.686506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0442688
Q10.380952
median1.133333
Q32.894737
95-th percentile8.53
Maximum12.166667
Range12.166667
Interquartile range (IQR)2.513785

Descriptive statistics

Standard deviation2.6414049
Coefficient of variation (CV)1.1980453
Kurtosis2.9450872
Mean2.2047621
Median Absolute Deviation (MAD)0.925
Skewness1.8448165
Sum548.98576
Variance6.9770199
MonotonicityNot monotonic
2025-07-09T23:33:01.831292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12
 
4.8%
0.333333 5
 
2.0%
1.791667 4
 
1.6%
0.583333 4
 
1.6%
0.916667 3
 
1.2%
0.75 3
 
1.2%
0.375 3
 
1.2%
0.208333 3
 
1.2%
3.416667 2
 
0.8%
3.458333 2
 
0.8%
Other values (183) 208
83.5%
ValueCountFrequency (%)
0 12
4.8%
0.043478 1
 
0.4%
0.045455 1
 
0.4%
0.05 1
 
0.4%
0.052632 1
 
0.4%
0.078261 1
 
0.4%
0.083333 1
 
0.4%
0.086957 1
 
0.4%
0.1 1
 
0.4%
0.104348 1
 
0.4%
ValueCountFrequency (%)
12.166667 1
0.4%
11.625 2
0.8%
10.952381 1
0.4%
10.916667 1
0.4%
10.166667 1
0.4%
9.958333 1
0.4%
9.583333 1
0.4%
9.391304 1
0.4%
9.083333 1
0.4%
8.708333 1
0.4%

SO2 1st Max Value
Real number (ℝ)

High correlation  Zeros 

Distinct40
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7012048
Minimum0
Maximum29
Zeros12
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-07-09T23:33:01.962094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.28
Q12
median5
Q39
95-th percentile20.6
Maximum29
Range29
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.1546631
Coefficient of variation (CV)0.91844127
Kurtosis1.375089
Mean6.7012048
Median Absolute Deviation (MAD)3.5
Skewness1.3202697
Sum1668.6
Variance37.879878
MonotonicityNot monotonic
2025-07-09T23:33:02.092827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
2 23
 
9.2%
4 22
 
8.8%
3 21
 
8.4%
5 19
 
7.6%
1 17
 
6.8%
9 16
 
6.4%
8 16
 
6.4%
6 14
 
5.6%
0 12
 
4.8%
10 8
 
3.2%
Other values (30) 81
32.5%
ValueCountFrequency (%)
0 12
4.8%
0.2 1
 
0.4%
0.4 2
 
0.8%
0.6 3
 
1.2%
0.7 1
 
0.4%
0.8 5
 
2.0%
0.9 5
 
2.0%
1 17
6.8%
1.1 1
 
0.4%
1.2 1
 
0.4%
ValueCountFrequency (%)
29 1
 
0.4%
28 1
 
0.4%
26 1
 
0.4%
24 2
0.8%
23 2
0.8%
22 3
1.2%
21 3
1.2%
20 1
 
0.4%
19 2
0.8%
18 3
1.2%

SO2 1st Max Hour
Real number (ℝ)

Zeros 

Distinct20
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.630522
Minimum0
Maximum23
Zeros41
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-07-09T23:33:02.247475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q321
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)19

Descriptive statistics

Standard deviation9.1099927
Coefficient of variation (CV)0.85696569
Kurtosis-1.6737348
Mean10.630522
Median Absolute Deviation (MAD)6
Skewness0.28187906
Sum2647
Variance82.991968
MonotonicityNot monotonic
2025-07-09T23:33:02.440650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 41
16.5%
23 29
11.6%
22 26
10.4%
5 26
10.4%
21 25
10.0%
6 22
8.8%
1 15
 
6.0%
7 14
 
5.6%
20 10
 
4.0%
8 8
 
3.2%
Other values (10) 33
13.3%
ValueCountFrequency (%)
0 41
16.5%
1 15
 
6.0%
2 7
 
2.8%
3 7
 
2.8%
4 7
 
2.8%
5 26
10.4%
6 22
8.8%
7 14
 
5.6%
8 8
 
3.2%
9 2
 
0.8%
ValueCountFrequency (%)
23 29
11.6%
22 26
10.4%
21 25
10.0%
20 10
 
4.0%
19 4
 
1.6%
18 1
 
0.4%
17 1
 
0.4%
16 1
 
0.4%
13 2
 
0.8%
10 1
 
0.4%

SO2 AQI
Real number (ℝ)

High correlation  Zeros 

Distinct28
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4497992
Minimum0
Maximum41
Zeros29
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-07-09T23:33:02.607627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q313
95-th percentile29.6
Maximum41
Range41
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.8844787
Coefficient of variation (CV)0.94017645
Kurtosis1.2312301
Mean9.4497992
Median Absolute Deviation (MAD)6
Skewness1.2607883
Sum2353
Variance78.933962
MonotonicityNot monotonic
2025-07-09T23:33:02.764714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 29
11.6%
3 23
 
9.2%
1 23
 
9.2%
6 22
 
8.8%
4 21
 
8.4%
7 19
 
7.6%
11 16
 
6.4%
13 16
 
6.4%
9 14
 
5.6%
14 8
 
3.2%
Other values (18) 58
23.3%
ValueCountFrequency (%)
0 29
11.6%
1 23
9.2%
3 23
9.2%
4 21
8.4%
6 22
8.8%
7 19
7.6%
9 14
5.6%
10 7
 
2.8%
11 16
6.4%
13 16
6.4%
ValueCountFrequency (%)
41 1
 
0.4%
40 1
 
0.4%
37 1
 
0.4%
34 2
0.8%
33 2
0.8%
31 3
1.2%
30 3
1.2%
29 1
 
0.4%
27 2
0.8%
26 3
1.2%

CO Units
Categorical

Constant 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size19.9 KiB
Parts per million
249 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters4233
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million 249
100.0%

Length

2025-07-09T23:33:02.920929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-09T23:33:03.012974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
parts 249
33.3%
per 249
33.3%
million 249
33.3%

Most occurring characters

ValueCountFrequency (%)
r 498
11.8%
i 498
11.8%
l 498
11.8%
498
11.8%
P 249
 
5.9%
s 249
 
5.9%
t 249
 
5.9%
a 249
 
5.9%
p 249
 
5.9%
m 249
 
5.9%
Other values (3) 747
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4233
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 498
11.8%
i 498
11.8%
l 498
11.8%
498
11.8%
P 249
 
5.9%
s 249
 
5.9%
t 249
 
5.9%
a 249
 
5.9%
p 249
 
5.9%
m 249
 
5.9%
Other values (3) 747
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4233
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 498
11.8%
i 498
11.8%
l 498
11.8%
498
11.8%
P 249
 
5.9%
s 249
 
5.9%
t 249
 
5.9%
a 249
 
5.9%
p 249
 
5.9%
m 249
 
5.9%
Other values (3) 747
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4233
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 498
11.8%
i 498
11.8%
l 498
11.8%
498
11.8%
P 249
 
5.9%
s 249
 
5.9%
t 249
 
5.9%
a 249
 
5.9%
p 249
 
5.9%
m 249
 
5.9%
Other values (3) 747
17.6%

CO Mean
Real number (ℝ)

High correlation 

Distinct189
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.91705747
Minimum0.1125
Maximum3.575
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-07-09T23:33:03.150209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1125
5-th percentile0.2849998
Q10.495833
median0.720833
Q31.233333
95-th percentile2.15
Maximum3.575
Range3.4625
Interquartile range (IQR)0.7375

Descriptive statistics

Standard deviation0.60752153
Coefficient of variation (CV)0.66246833
Kurtosis2.1010694
Mean0.91705747
Median Absolute Deviation (MAD)0.283333
Skewness1.4502598
Sum228.34731
Variance0.36908241
MonotonicityNot monotonic
2025-07-09T23:33:03.352746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.566667 4
 
1.6%
0.570833 4
 
1.6%
0.6 4
 
1.6%
0.795833 4
 
1.6%
0.725 4
 
1.6%
0.658333 3
 
1.2%
1.829167 3
 
1.2%
0.483333 3
 
1.2%
0.495833 3
 
1.2%
1.079167 2
 
0.8%
Other values (179) 215
86.3%
ValueCountFrequency (%)
0.1125 1
0.4%
0.170833 1
0.4%
0.175 1
0.4%
0.191667 1
0.4%
0.204167 1
0.4%
0.2125 1
0.4%
0.220833 1
0.4%
0.233333 1
0.4%
0.245833 1
0.4%
0.25 1
0.4%
ValueCountFrequency (%)
3.575 1
0.4%
2.958333 1
0.4%
2.891667 1
0.4%
2.7875 1
0.4%
2.7 1
0.4%
2.65 1
0.4%
2.533333 1
0.4%
2.366667 1
0.4%
2.35 1
0.4%
2.316667 1
0.4%

CO 1st Max Value
Real number (ℝ)

High correlation 

Distinct48
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6253012
Minimum0.2
Maximum5.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-07-09T23:33:03.572819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.4
Q10.8
median1.3
Q32.1
95-th percentile4.16
Maximum5.3
Range5.1
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.15114
Coefficient of variation (CV)0.70826259
Kurtosis0.90541997
Mean1.6253012
Median Absolute Deviation (MAD)0.6
Skewness1.244596
Sum404.7
Variance1.3251234
MonotonicityNot monotonic
2025-07-09T23:33:03.777282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.7 17
 
6.8%
0.6 16
 
6.4%
0.8 16
 
6.4%
1.4 14
 
5.6%
1 14
 
5.6%
0.9 14
 
5.6%
0.5 12
 
4.8%
1.1 11
 
4.4%
2.1 9
 
3.6%
0.4 9
 
3.6%
Other values (38) 117
47.0%
ValueCountFrequency (%)
0.2 2
 
0.8%
0.3 4
 
1.6%
0.4 9
3.6%
0.5 12
4.8%
0.6 16
6.4%
0.7 17
6.8%
0.8 16
6.4%
0.9 14
5.6%
1 14
5.6%
1.1 11
4.4%
ValueCountFrequency (%)
5.3 1
 
0.4%
5.1 1
 
0.4%
5 1
 
0.4%
4.9 2
0.8%
4.8 2
0.8%
4.6 1
 
0.4%
4.5 2
0.8%
4.2 3
1.2%
4.1 1
 
0.4%
4 1
 
0.4%

CO 1st Max Hour
Real number (ℝ)

Zeros 

Distinct17
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.37751
Minimum0
Maximum23
Zeros28
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-07-09T23:33:03.938618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q311
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.1266472
Coefficient of variation (CV)0.97005521
Kurtosis-0.60227319
Mean8.37751
Median Absolute Deviation (MAD)4
Skewness0.92135419
Sum2086
Variance66.042395
MonotonicityNot monotonic
2025-07-09T23:33:04.087754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
23 46
18.5%
2 30
12.0%
0 28
11.2%
1 23
9.2%
8 18
 
7.2%
6 18
 
7.2%
7 17
 
6.8%
3 16
 
6.4%
9 14
 
5.6%
4 11
 
4.4%
Other values (7) 28
11.2%
ValueCountFrequency (%)
0 28
11.2%
1 23
9.2%
2 30
12.0%
3 16
6.4%
4 11
 
4.4%
5 3
 
1.2%
6 18
7.2%
7 17
6.8%
8 18
7.2%
9 14
5.6%
ValueCountFrequency (%)
23 46
18.5%
22 3
 
1.2%
21 3
 
1.2%
19 1
 
0.4%
12 3
 
1.2%
11 7
 
2.8%
10 8
 
3.2%
9 14
 
5.6%
8 18
 
7.2%
7 17
 
6.8%

CO AQI
Real number (ℝ)

High correlation 

Distinct48
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.48996
Minimum2
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-07-09T23:33:04.261386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q19
median15
Q324
95-th percentile47.6
Maximum59
Range57
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.000035
Coefficient of variation (CV)0.70308616
Kurtosis0.81587236
Mean18.48996
Median Absolute Deviation (MAD)7
Skewness1.2266761
Sum4604
Variance169.00091
MonotonicityNot monotonic
2025-07-09T23:33:04.489089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
8 17
 
6.8%
7 16
 
6.4%
9 16
 
6.4%
16 14
 
5.6%
11 14
 
5.6%
10 14
 
5.6%
6 12
 
4.8%
13 11
 
4.4%
24 9
 
3.6%
5 9
 
3.6%
Other values (38) 117
47.0%
ValueCountFrequency (%)
2 2
 
0.8%
3 4
 
1.6%
5 9
3.6%
6 12
4.8%
7 16
6.4%
8 17
6.8%
9 16
6.4%
10 14
5.6%
11 14
5.6%
13 11
4.4%
ValueCountFrequency (%)
59 1
 
0.4%
57 1
 
0.4%
56 1
 
0.4%
55 2
0.8%
54 2
0.8%
52 1
 
0.4%
51 2
0.8%
48 3
1.2%
47 1
 
0.4%
45 1
 
0.4%

Interactions

2025-07-09T23:32:54.547945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:23.280271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:25.894452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:27.599785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:29.501015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:31.063138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:32.912117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:34.541077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:37.049037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:39.250623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:40.887527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:42.905093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:44.957059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:46.724884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:48.272588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:51.025959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:53.009644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:54.643717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:23.448738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:25.992419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:27.688703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:29.593938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:31.156651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:33.009094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:34.638655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:37.173939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:39.349232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:40.982287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:43.007340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:45.049325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:46.811575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:48.367568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:51.156419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:53.100974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:54.737492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:23.590166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:26.093017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:27.786229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:29.681269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:31.258909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:33.123066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:34.748114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:37.319422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:39.461252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:41.088154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:43.108458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:45.147621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-09T23:32:42.242261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:44.676865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:46.454626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:48.001909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:50.568900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:52.735091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:54.280235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:56.036410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:25.605620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:27.410763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:29.302077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:30.877550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:32.730739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:34.357795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:36.756617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:39.069420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:40.712122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:42.727600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:44.769463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:46.542064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:48.088611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:50.707169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:52.820441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:54.369011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:56.137980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:25.767890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:27.505378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:29.395757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:30.977217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:32.820854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:34.444667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:36.898914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:39.152599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:40.797150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:42.812447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:44.863027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:46.621780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:48.176500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:50.853663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:52.908334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-09T23:32:54.458919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-09T23:33:04.694454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CO 1st Max HourCO 1st Max ValueCO AQICO MeanNO2 1st Max HourNO2 1st Max ValueNO2 AQINO2 MeanO3 1st Max HourO3 1st Max ValueO3 AQIO3 MeanSO2 1st Max HourSO2 1st Max ValueSO2 AQISO2 MeanUnnamed: 0
CO 1st Max Hour1.000-0.167-0.167-0.1840.2480.0410.040-0.0010.0410.0060.0050.0010.2560.0410.041-0.0070.054
CO 1st Max Value-0.1671.0001.0000.969-0.0210.5710.5710.659-0.237-0.443-0.445-0.6320.1850.6890.6760.669-0.662
CO AQI-0.1671.0001.0000.969-0.0210.5710.5710.659-0.237-0.443-0.445-0.6320.1850.6890.6760.669-0.662
CO Mean-0.1840.9690.9691.000-0.0310.5490.5490.691-0.244-0.483-0.484-0.6760.1750.6670.6550.661-0.649
NO2 1st Max Hour0.248-0.021-0.021-0.0311.0000.1760.1740.0730.011-0.075-0.074-0.1290.3480.0410.0390.021-0.049
NO2 1st Max Value0.0410.5710.5710.5490.1761.0001.0000.877-0.065-0.240-0.241-0.4230.2510.3940.3790.381-0.373
NO2 AQI0.0400.5710.5710.5490.1741.0001.0000.877-0.065-0.241-0.241-0.4230.2500.3940.3790.381-0.373
NO2 Mean-0.0010.6590.6590.6910.0730.8770.8771.000-0.144-0.469-0.469-0.6600.1900.4840.4700.514-0.468
O3 1st Max Hour0.041-0.237-0.237-0.2440.011-0.065-0.065-0.1441.0000.2780.2780.249-0.086-0.265-0.270-0.3090.268
O3 1st Max Value0.006-0.443-0.443-0.483-0.075-0.240-0.241-0.4690.2781.0001.0000.889-0.072-0.411-0.409-0.4040.601
O3 AQI0.005-0.445-0.445-0.484-0.074-0.241-0.241-0.4690.2781.0001.0000.889-0.074-0.412-0.410-0.4050.601
O3 Mean0.001-0.632-0.632-0.676-0.129-0.423-0.423-0.6600.2490.8890.8891.000-0.149-0.522-0.513-0.5260.599
SO2 1st Max Hour0.2560.1850.1850.1750.3480.2510.2500.190-0.086-0.072-0.074-0.1491.0000.3310.3220.267-0.252
SO2 1st Max Value0.0410.6890.6890.6670.0410.3940.3940.484-0.265-0.411-0.412-0.5220.3311.0000.9990.940-0.432
SO2 AQI0.0410.6760.6760.6550.0390.3790.3790.470-0.270-0.409-0.410-0.5130.3220.9991.0000.937-0.423
SO2 Mean-0.0070.6690.6690.6610.0210.3810.3810.514-0.309-0.404-0.405-0.5260.2670.9400.9371.000-0.432
Unnamed: 00.054-0.662-0.662-0.649-0.049-0.373-0.373-0.4680.2680.6010.6010.599-0.252-0.432-0.423-0.4321.000

Missing values

2025-07-09T23:32:56.346160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-09T23:32:56.609346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
1141330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-01Parts per billion19.04166749.01946Parts per million0.0225000.0401034Parts per billion3.0000009.02113.0Parts per million0.8789472.22325.0
5541330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-02Parts per billion22.95833336.01934Parts per million0.0133750.0321027Parts per billion1.9583333.0224.0Parts per million1.0666672.3026.0
9941330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-03Parts per billion38.12500051.0848Parts per million0.0079580.016914Parts per billion5.25000011.01916.0Parts per million1.7625002.5828.0
131341330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-04Parts per billion40.26087074.0872Parts per million0.0141670.033928Parts per billion7.08333316.0823.0Parts per million1.8291673.02334.0
171741330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-05Parts per billion48.45000061.02258Parts per million0.0066670.012910Parts per billion8.70833315.0721.0Parts per million2.7000003.7242.0
212141330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-06Parts per billion39.95000073.0871Parts per million0.0117500.0251021Parts per billion6.76190517.0724.0Parts per million2.3083333.6941.0
252541330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-07Parts per billion29.62500043.0941Parts per million0.0116250.0241020Parts per billion8.66666721.0730.0Parts per million1.8291673.52340.0
292941330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-08Parts per billion29.66666741.0039Parts per million0.0097500.0201017Parts per billion8.25000018.0026.0Parts per million2.7875005.1257.0
333341330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-09Parts per billion25.08333337.02035Parts per million0.0107920.0221019Parts per billion6.50000013.01919.0Parts per million1.6750002.8232.0
373741330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-10Parts per billion37.66666770.02068Parts per million0.0084580.015913Parts per billion9.95833321.02030.0Parts per million2.1791673.72342.0
Unnamed: 0State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
96196141330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-09-07Parts per billion35.23809558.01955Parts per million0.0197080.0571048Parts per billion1.7916676.039.0Parts per million0.7958331.2714.0
96596541330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-09-08Parts per billion29.16666742.02240Parts per million0.0182080.0461139Parts per billion0.9166673.064.0Parts per million0.6125000.91110.0
96996941330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-09-09Parts per billion26.87500058.02055Parts per million0.0219170.0521044Parts per billion0.8750002.033.0Parts per million0.7083331.0411.0
97397341330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-09-10Parts per billion17.62500037.02235Parts per million0.0306250.0651067Parts per billion0.2916671.001.0Parts per million0.5708331.1113.0
97797741330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-09-11Parts per billion25.85000041.02139Parts per million0.0172920.0411035Parts per billion1.0434784.056.0Parts per million0.6000001.01111.0
98198141330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-09-12Parts per billion32.00000058.01955Parts per million0.0184580.0481041Parts per billion1.9583336.059.0Parts per million0.8416671.3615.0
98598541330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-09-13Parts per billion40.54166773.01971Parts per million0.0070000.0241420Parts per billion2.6818189.0113.0Parts per million1.4823532.1424.0
98998941330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-09-14Parts per billion37.66666759.02156Parts per million0.0227500.0531045Parts per billion2.6250006.009.0Parts per million0.7541671.4116.0
99399341330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-09-15Parts per billion37.19047661.02058Parts per million0.0226250.0531045Parts per billion2.4166678.0011.0Parts per million0.7125001.32315.0
99799741330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-09-16Parts per billion34.70833379.02278Parts per million0.0325000.07610101Parts per billion1.3333335.0227.0Parts per million0.6666671.5117.0